Weighted move rating for AI
It can make sense to incorporate the standard deviation of Monte-Carlo-based evaluations in some ways to reduce risk, but I don't think the way of using it that you described would work well.

For MCTS evaluations, if you're taking a zero-sum-style approach (which you are if you are trying to estimate win/loss probabilities), it is very important that your estimates are "symmetric" with respect to the players. If you evaluate the probability of winning for player A to be $p$, it is important that your algorithm simultaneously evaluates the probability of the other player B to be equal to $1 - p$ (or, if you prefer winning chance, if the chance of winning for one player is $p\%$, it should be $100\% - p\%$ for the other player). This appears to be violated by your idea, which subtracts standard deviation regardless of player "perspective".

A better place to take standard deviation (or, similarly, variance) into account would be, for example, the Selection phase of MCTS. The most common strategy for the Selection phase is using the "UCB1 equation". You can modify that to include the variance in your observations, for example using the "UCB1-Tuned" strategy as described in the beginning of Section 4 of Finite-time Analysis of the Multiarmed Bandit Problem.



In my answer above, I assume that you were talking about evaluations "inside" the algorithm, while it is still running. If you were rather thinking of the final move selection for the "real" game after having run the algorithm for a while, the most common approach there is to simply play the move with the maximum number of visits (also referred to as robust child), rather than playing the move with the maximum score. It should not be necessary to include standard deviation at this stage anymore.